@inproceedings{yu-etal-2025-ca,
    title = "{CA}-{GAR}: Context-Aware Alignment of {LLM} Generation for Document Retrieval",
    author = "Yu, Heng  and
      Kang, Junfeng  and
      Li, Rui  and
      Liu, Qi  and
      He, Liyang  and
      Huang, Zhenya  and
      Shen, Shuanghong  and
      Lu, Junyu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.303/",
    pages = "5836--5849",
    ISBN = "979-8-89176-256-5",
    abstract = "Information retrieval has evolved from traditional sparse and dense retrieval methods to approaches driven by large language models (LLMs). Recent techniques, such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieval (GDR), leverage LLMs to enhance retrieval but face key challenges: GAR{'}s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLMs by constraining outputs to predefined document identifiers. To address these issues, we propose Context-Aware Generation-Augmented Retrieval (CA-GAR), which enhances LLMs by integrating corpus information into their generation process. CA-GAR optimizes token selection by incorporating relevant document information and leverages a Distribution Alignment Strategy to extract corpus information using a lexicon-based approach. Experimental evaluations on seven tasks from the BEIR benchmark and four non-English languages from Mr.TyDi demonstrate that CA-GAR outperforms existing methods."
}